Damage prediction of rear plate in Whipple shields based on machine learning method

A typical Whipple shield consists of double-layered plates with a certain gap. The space debris impacts the outer plate and is broken into a debris cloud (shattered, molten, vaporized) with dispersed energy and momentum, which reduces the risk of penetrating the bulkhead. In the realm of hypervelocity impact, strain rate (> 10⁵ s⁻¹) effects are negligible, and fluid dynamics is employed to describe the impact process. Efficient numerical tools for precisely predicting the damage degree can greatly accelerate the design and optimization of advanced protective structures. Current hypervelocity impact research primarily focuses on the interaction between projectile and front plate and the movement of debris cloud. However, the damage mechanism of debris cloud impacts on rear plates - the critical threat component - remains underexplored owing to complex multi-physics processes and prohibitive computational costs. Existing approaches, ranging from semi-empirical equations to a machine learning-based ballistic limit prediction method, are constrained to binary penetration classification. Alternatively, the uneven data from experiments and simulations caused these methods to be ineffective when the projectile has irregular shapes and complicate flight attitude. Therefore, it is urgent to develop a new damage prediction method for predicting the rear plate damage, which can help to gain a deeper understanding of the damage mechanism. In this study, a machine learning (ML) method is developed to predict the damage distribution in the rear plate. Based on the unit velocity space, the discretized information of debris cloud and rear plate damage from rare simulation cases is used as input data for training the ML models, while the generalization ability for damage distribution prediction is tested by other simulation cases with different attack angles. The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). The RF-based model effectively identifies damage features in sparsely distributed debris cloud and cumulative effect. This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy. Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features, enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.